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From |
"Michael N. Mitchell" <[email protected]> |

To |
[email protected] |

Subject |
Re: st: xi3 / nlogit |

Date |
Sat, 04 Dec 2010 18:33:50 -0800 |

Dear Nils

Best regards, Michael N. Mitchell Data Management Using Stata - http://www.stata.com/bookstore/dmus.html A Visual Guide to Stata Graphics - http://www.stata.com/bookstore/vgsg.html Stata tidbit of the week - http://www.MichaelNormanMitchell.com On 2010-12-04 8.58 AM, Nils Wlömert wrote:

Many thanks Michael, the definition of the effect coded variables works great now. However, I think there is another issue related to variable coding in the model (empty cells that are coded as zeros). I estimated the asclogit choice model (6 attributes with 4 levels each + no-chocie base option) in Stata with linear coding of all attributes and the results are identical to the results obtained for the 1 segment solution in Latent Gold Choice which als uses McFadden's choice model: Attributes p_ppd (price I) -0,4507 p_fl (price II) -0,8754 p_drm -0,1731 fl_drm -0,5922 ad -0,2764 cat 0,2817 nobuy -1,6554 When it comes to effect coding, there are some difficulties in Stata. I first define the effect coded variables as you suggested (price attributes remain linear coded): . xi3 e.p_drm e.fl_drm e.ad e.cat e.nobuy e.p_drm _Ip_drm_0-4 (naturally coded; _Ip_drm_0 omitted) e.fl_drm _Ifl_drm_0-4 (naturally coded; _Ifl_drm_0 omitted) e.ad _Iad_0-4 (naturally coded; _Iad_0 omitted) e.cat _Icat_0-4 (naturally coded; _Icat_0 omitted) e.nobuy _Inobuy_0-1 (naturally coded; _Inobuy_0 omitted) When trying to estimate asclogit, some levels are dropped because of collinearity and the model does not coverge. asclogit decision p_ppd p_fl _Ip_drm_1 _Ip_drm_2 _Ip_drm_3 _Ip_drm_4 _Ifl_drm_1 _Ifl_drm_2 _Ifl_drm_3 _Ifl_drm_4 _Iad_1 _Iad_2 _Iad_3 _Iad_4 _Icat_1 _Icat_2 _Icat_3 _Icat_4 _Inobuy_1, noconst case(case) alternatives(alternative) note: _Icat_4 dropped because of collinearity note: _Iad_4 dropped because of collinearity note: model has collinear variables; convergence may not be achieved This is somewhat surprising as the model is estimated without any difficulties in Latent Gold: Attributes: p_ppd (price I - linear) -0,3540 p_fl (price II - linear) -1,1689 p_drm (effect) 1 0,4179 2 -0,2083 3 0,2317 4 -0,4412 fl_drm (effect) 1 0,5906 2 0,3027 3 -0,2918 4 -0,6015 ad (effect) 1 0,4328 2 0,1001 3 -0,0839 4 -0,4489 cat (effect) 1 -0,5570 2 -0,0606 3 0,2858 4 0,3318 nobuy (effect) 0 0,4390 1 -0,4390 The same thing happens using nlogit: nlogit decision p_ppd p_fl _Ip_drm_1 _Ip_drm_2 _Ip_drm_3 _Ip_drm_4 _Ifl_drm_1 _Ifl_drm_2 _Ifl_drm_3 _Ifl_drm_4 _Iad_1 _Iad_2 _Iad_3 _Iad_4 _Icat_1 _Icat_2 _Icat_3 _Icat_4 _Inobuy_1 || type: || alternative:, noconst case(case) note: _Iad_2 dropped because of collinearity note: _Icat_2 dropped because of collinearity note: the model specified for level 2 has collinear variables; convergence may not be achieved I assume that this difference is related to the coding of the effect coded attributes as the model is specified identically in Stata and Latent Gold otherwise. Note that the model includes alternative specific attributes (e.g., price I & II) which means that some attributes are NOT included in all alternatives (i.e., there are empty cells in the data set if an attribute is not included in an alternative). Also, all cells of the attribute levels are 'empty' in the lines representing the 'no-choice' alternative . Latent Gold treats these cells as 'empty' and returns the part-worths for each level of the effect coded attributes without omitting a level (see above). In Stata, these 'empty' cells are replaced by zeros and the 0-levels are omitted when the model is estimated (see above). I would be grateful if you could give some advice on how to define the effect coded variables correctly. Especially with regard to the empty cells that are coded as zeros and the handling of collinerity. Thanks & best, Nils Am 04.12.2010 um 01:31 schrieb Michael Mitchell:Dear Nils When I wrote -xi3-, I don't think these models existed (or if they did, I did not have them in mind). So, unfortunately, -xi3- does not immediately work with programs like -xtmixed-, or -nlogit-, because it gets confused by the pipes -||-. However, you can still use -xi3- if you do it in a two step process. For example, here is using -xi3- in a one step process to do a regression . sysuse auto (1978 Automobile Data) . xi3: regress price e.rep78 g.foreign e.rep78 _Irep78_1-5 (naturally coded; _Irep78_1 omitted) g.foreign _Iforeign_0-1 (naturally coded; _Iforeign_0 omitted) Source | SS df MS Number of obs = 69 -------------+------------------------------ F( 5, 63) = 0.19 Model | 8372481.37 5 1674496.27 Prob > F = 0.9670 Residual | 568424478 63 9022610.75 R-squared = 0.0145 -------------+------------------------------ Adj R-squared = -0.0637 Total | 576796959 68 8482308.22 Root MSE = 3003.8 ------------------------------------------------------------------------------ price | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- _Irep78_2 | 188.879 1024.352 0.18 0.854 -1858.124 2235.882 _Irep78_3 | 646.8116 710.873 0.91 0.366 -773.7548 2067.378 _Irep78_4 | 274.3754 799.3802 0.34 0.733 -1323.059 1871.809 _Irep78_5 | 104.1799 1036.513 0.10 0.920 -1967.126 2175.486 _Iforeign_1 | 36.7572 1010.484 0.04 0.971 -1982.533 2056.048 _cons | 5797.125 581.597 9.97 0.000 4634.896 6959.353 ------------------------------------------------------------------------------ Instead, we can first use -xi3- to create the coded variables.... . xi3 e.rep78 g.foreign e.rep78 _Irep78_1-5 (naturally coded; _Irep78_1 omitted) g.foreign _Iforeign_0-1 (naturally coded; _Iforeign_0 omitted) And then we can include the coded variables into the model, as shown below. . regress price _Irep78_2 _Irep78_3 _Irep78_4 _Irep78_5 _Iforeign_1 Source | SS df MS Number of obs = 69 -------------+------------------------------ F( 5, 63) = 0.19 Model | 8372481.37 5 1674496.27 Prob > F = 0.9670 Residual | 568424478 63 9022610.75 R-squared = 0.0145 -------------+------------------------------ Adj R-squared = -0.0637 Total | 576796959 68 8482308.22 Root MSE = 3003.8 ------------------------------------------------------------------------------ price | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- _Irep78_2 | 188.879 1024.352 0.18 0.854 -1858.124 2235.882 _Irep78_3 | 646.8116 710.873 0.91 0.366 -773.7548 2067.378 _Irep78_4 | 274.3754 799.3802 0.34 0.733 -1323.059 1871.809 _Irep78_5 | 104.1799 1036.513 0.10 0.920 -1967.126 2175.486 _Iforeign_1 | 36.7572 1010.484 0.04 0.971 -1982.533 2056.048 _cons | 5797.125 581.597 9.97 0.000 4634.896 6959.353 ------------------------------------------------------------------------------ I know it is a kludge, but I hope it works for you. Best regards, Michael N. Mitchell Data Management Using Stata - http://www.stata.com/bookstore/dmus.html A Visual Guide to Stata Graphics - http://www.stata.com/bookstore/vgsg.html Stata tidbit of the week - http://www.MichaelNormanMitchell.com On Fri, Dec 3, 2010 at 4:07 PM, Nils Wlömert <[email protected]> wrote:Dear listers, I would like to use effect-coding (via xi3) with nlogit: xi3: nlogit decision p_ppd p_fl e.p_drm e.fl_drm e.ad e.cat e.nobuy || type: || alternative:, noconst case(case_1) However, I get the following error: "|" invalid name r(198); Does xi3 work with nested models at all? Estimation works fine without effect-coding via xi3. Many thanks! Nils * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/* * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/* * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

* * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: xi3 / nlogit***From:*Nils Wlömert <[email protected]>

**Re: st: xi3 / nlogit***From:*Michael Mitchell <[email protected]>

**Re: st: xi3 / nlogit***From:*Nils Wlömert <[email protected]>

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